Machine learning models such as neural networks are used in many applications. In certain situations, it may be desirable to train specialized neural networks to perform specialized tasks. For example, in the application of financial market prediction, it may be desirable to train a price prediction model for each individual stock, rather than relying on a generic price prediction model that is not specifically trained for any individual stock. However, training a neural network from scratch may be slow and time-consuming. One approach is to use transfer learning.
In transfer learning, a base model trained for a first task is reused as the starting point for training of a model for a second task. Therefore, there is a need for the base model to be prepared in a way that would facilitate effective and efficient training for the second task.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section